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AMSR-E Snow: Can Snowfall Help Improve SWE Estimates?

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  • 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
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Abstract

Snowfall and snowpack are tightly coupled within the snow water cycle and careful monitoring is crucial to better understand snow’s role in Earth’s water and energy cycles. Current and future estimates of the total amount of seasonal snow on the ground are limited by the variability in the initial snowfall and uncertainties in in situ and remote sensing observations. In this study, passive microwave remote sensing estimates of snowfall and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer (AMSR-E) instrument are used to assess the consistency in the snow products. A snow evolution model, SnowModel, is employed to simulate snow processes that occur between the initial snowfall and subsequent SWE. AMSR-E is found to have significant discrepancies in both snowfall and SWE compared to MERRA-2 reanalysis and the Canadian Meteorological Centre (CMC) snow product. It is shown that AMSR-E snowfall is currently not a useful metric to estimate SWE without applying large corrections throughout the winter season. Regions of consistency in the AMSR-E snow products occur for reasons that pertain to underestimation in both snowfall and SWE. In addition to snow consistency, microwave brightness temperatures (TBs) are analyzed in response to the snowpack and snowfall physical properties. These experiments indicate significant sensitivity to regime-dependent scattering characteristics that must be accounted for to accurately estimate global snow properties and provide better physical consistency in the snow products from remote sensing platforms.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ryan Gonzalez, ryan.gonzalez@colostate.edu

Abstract

Snowfall and snowpack are tightly coupled within the snow water cycle and careful monitoring is crucial to better understand snow’s role in Earth’s water and energy cycles. Current and future estimates of the total amount of seasonal snow on the ground are limited by the variability in the initial snowfall and uncertainties in in situ and remote sensing observations. In this study, passive microwave remote sensing estimates of snowfall and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer (AMSR-E) instrument are used to assess the consistency in the snow products. A snow evolution model, SnowModel, is employed to simulate snow processes that occur between the initial snowfall and subsequent SWE. AMSR-E is found to have significant discrepancies in both snowfall and SWE compared to MERRA-2 reanalysis and the Canadian Meteorological Centre (CMC) snow product. It is shown that AMSR-E snowfall is currently not a useful metric to estimate SWE without applying large corrections throughout the winter season. Regions of consistency in the AMSR-E snow products occur for reasons that pertain to underestimation in both snowfall and SWE. In addition to snow consistency, microwave brightness temperatures (TBs) are analyzed in response to the snowpack and snowfall physical properties. These experiments indicate significant sensitivity to regime-dependent scattering characteristics that must be accounted for to accurately estimate global snow properties and provide better physical consistency in the snow products from remote sensing platforms.

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Corresponding author: Ryan Gonzalez, ryan.gonzalez@colostate.edu
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